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                <span class="date">2019-05-20</span>
            
            
            
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            <p>首先 <a href="zhangrunfa.gitee.io">网站展示</a></p>
<p>###1. 使用Hexo生成静态网页<a href="http://zwd596257180.gitee.io/blog/2019/04/15/hexo_manong_bog/" target="_blank" rel="noopener">参考</a></p>
<p>###2. 将网页部署到码云：<a href="https://blog.csdn.net/qq_36667170/article/details/79318578" target="_blank" rel="noopener">码云Gitee pages静态页面展示</a></p>
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<p><strong><em>参考链接:</em></strong><br><a href="https://blog.csdn.net/q2158798/article/details/80154725" target="_blank" rel="noopener">用码云搭建简单的个人个性博客</a><br><a href="https://blog.csdn.net/qq_20954959/article/details/54425916" target="_blank" rel="noopener">码云Hexo+OSChina</a><br><strong><em>遇到的坑</em></strong>：<br>hexo安装不上，先看<a href="https://blog.csdn.net/Lsavage/article/details/87522367" target="_blank" rel="noopener">这个</a>，不够详细<a href="https://blog.csdn.net/qq_27093465/article/details/72954725" target="_blank" rel="noopener">再看这个</a><br><strong><em>后记:</em></strong><br>最近注册了www.润发.xyz域名，挖个坑立个flag，后期部署到自己的网站上。教程随后更新。</p>
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<p>If you are interested in this topic.<br>You can get in touch with me.<br>18234056952(Tel wechat qq)</p>

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            <p>（语言和平台没有高下之分，标题党罢了~）</p>
<p>符号计算大咖Mathematica刚宣布为Python推出Wolfram库。</p>
<p>从此数值计算和符号计算终于不用跨平台了！<br><img src="http://upload-images.jianshu.io/upload_images/17163699-d096f4d9ca88488b.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1080/q/50" alt="图片发自简书App"></p>
<p><img src="http://upload-images.jianshu.io/upload_images/17163699-d54c5664aed16333.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1080/q/50" alt="图片发自简书App"></p>
<p><img src="http://upload-images.jianshu.io/upload_images/17163699-d56a55ccdaffb329.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1080/q/50" alt="图片发自简书App"></p>
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<h2 id="官宣地址-介绍"><a href="#官宣地址-介绍" class="headerlink" title="官宣地址 +介绍"></a>官宣<a href="https://blog.wolfram.com/2019/05/16/announcing-the-wolfram-client-library-for-python/" target="_blank" rel="noopener">地址</a> +<a href="https://reference.wolfram.com/language/WolframClientForPython/" target="_blank" rel="noopener">介绍</a></h2><p>If you are interested in this topic.<br>You can get in touch with me.<br>18234056952(Tel wechat qq)</p>

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                <span class="date">2019-05-20</span>
            
            
            
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<li>云主机资源的分配。<br>背景:随着5G发展，网费下降，任意地点办公会越来越普及，双面屏手机只需链接自己的云电脑即可随时办公。<br>AI预测用户使用时段，分配资源</li>
<li>PDEs</li>
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<p>If you are interested in this topic.<br>You can get in touch with me.<br>18234056952(Tel wechat qq)</p>

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          <p>1<a href="http://www.cnblogs.com/xitingxie/p/8336137.html" target="_blank">数据表生成，数据表查看，和数据清洗</a></p><p><a href="http://www.cnblogs.com/xitingxie/p/8360038.html" target="_blank">2数据预处理，数据提取和数据筛选</a></p><p><a href="http://www.cnblogs.com/xitingxie/p/8412909.html" target="_blank">3数据汇总，数据统计和数据输出</a></p><hr id="null"><p><a href="https://www.jianshu.com/p/360a9bf5fb14" target="_blank">Python控制Excel</a>实现办公自动化</p><hr id="null"><p><a href="https://www.cnblogs.com/zzhzhao/p/5269217.html" target="_blank">python数据分析</a></p><hr id="null"><p>If you are interested in this topic.﻿<br>You can get in touch with me.﻿<br>18234056952(Tel  wechat  qq)</p>
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          <p>1<a href="http://bluewhale.cc/2016-08-15/python-merge.html" target="_blank">数据匹配</a>2<a href="http://bluewhale.cc/2016-08-06/use-pandas-filter-and-sort.html" target="_blank">筛选排序</a>3<a href="http://bluewhale.cc/2016-08-04/use-pandas-create-a-pivot-table.html" target="_blank">创建数据透视表</a></p><hr><p>pandas 实现 Excel <a href="https://www.jianshu.com/p/e664b9a3bf70" target="_blank">常见操作 (1)</a> ，<a href="https://www.jianshu.com/p/66d2e68b726f" target="_blank">常见操作 (2)</a></p><hr id="null"><p><a href="https://www.jianshu.com/p/0f6749c23d0f" target="_blank">pandas基础教程</a>+<a href="https://www.jianshu.com/p/5aa6579f70b1" target="_blank">一个月整理的pandas教程</a>+<a href="https://If%20you%20are%20interested%20in%20this%20topic." target="_blank">Python合并两列</a>+<a href="https://www.jianshu.com/p/f0cb3e9cb0ad" target="_blank">pandas小技巧</a></p><hr><p>If you are interested in this topic.﻿<br>You can get in touch with me.﻿<br>18234056952(Tel  wechat  qq)</p>
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<p>######导入 导出###############<br>df=pd.read_excel('D:/1.xls')<br>df.to_csv("D:/test.csv")<br>#没有后缀的数据或奇怪的数据<br>pd.read_table('C:/Users/fafa/Desktop/testA/user_data')<br>pd.read_csv('testA/totalExposureLog.out',sep='\t',names=['ad_request_id','ad_request_time','ad_space_id','user_id','ad_id','Exposure_ad_size','bid','pctr','quality_ecpm','totalEcpm'],memory_map=True)<br>#### 导入Excel的sheet #############<br> # 方法一：通过 index 指定工作表<br>df3 = pd.read_excel(file_name, sheet_name=0)<br> # 方法二：指定工作表名称<br>df4 = pd.read_excel(file_name, sheet_name='Sheet1')<br>#### 导出Excel的sheet #############<br>#① 导出带有sheet的csv（只能有1个sheet）<br>df1.to_csv("3.csv",mode='a',index=False)<br>#② 导出带有sheet的excel<br>##  命名待被导入的文件<br>writer = pd.ExcelWriter("2.xlsx")<br>##  命名待被导入的sheet ###<br>df1.to_excel(writer,sheet_name='公司维度表',index=False)<br>df2.to_excel(writer, sheet_name='货物维度表',index=False)<br>writer.save()<br>writer.close()</p>
<p><a href="https://share.weiyun.com/55p7VIq" target="_blank">#大文件分批读取并导出为多个小文件</a>，<a href="https://share.weiyun.com/5xV3hNb" target="_blank">方法2</a><br>#或者可以快速做一个DataFrame<br>df4 = pd.DataFrame({'col1':['1',3],'col2':[2,4]},index=['a','b'])</p>
<p>#大文件导入导入数据，只选取前100行和特定几列。<br>subset_columns = ['Job #', 'Doc #', 'Borough', 'Initial Cost', 'Total Est. Fee']<br>df = pd.read_csv('文件路径', nrows=100, usecols=subset_columns)<br>df.head()</p>
<p>#################查看数据量#################<br>df.describe()<br>df.count()<br>#<a href="https://blog.csdn.net/Arwen_H/article/details/81516204" target="_blank">二、查看数据常用方法</a><br>#查看列名<br>df.columns</p>
<p><br></p>
<p>##################       <a href="https://blog.csdn.net/Arwen_H/article/details/81587379" target="_blank">列名修改 索引修改 内容修改 数据类型转换 时间转换</a>    ###############</p>
<p>##########给没有表头的数据补上列名 或者直接暴力更改列名##################<br>df.columns=['广告id','创建时间','广告账户id','商品id','商品类型','广告行业id','素材尺寸']<br>##########更改列名更改列名更改列名################<br>dataA=df.rename(columns={"姓名":"最高分得主A","得分":"A题"})<br>########选择需要的特征，更改列的顺序##############<br>df1=df1[['广告id','创建时间','商品id', '商品类型', '广告行业id', '素材尺寸']]</p>
<p><br></p>
<p>######################### 关联两个表 merge ########################## <br>df3=pd.merge(df1,df2,on='广告id')<br>#########################合并两个表concat###########################<br>###把"赛题A"增到"A总"############<br>df1=pd.read_excel('D:/电专编程大赛每日数据/编程大赛A题.xls',header=0)<br>df2=pd.read_excel('D:/电专编程大赛每日数据/A总.xls',header=0)<br>#合并每日新数据df1到总表df2:<br>dfA=pd.concat([df1,df2]).drop_duplicates()<br>#去重(按照ID)<br>dfA=dfA.drop_duplicates("ID")<br>#重置索引<br>dfA=dfA.reset_index(drop=True)<br>#导出总表dfA: <br> dfA.to_excel("D:/电专编程大赛每日数据/A总.xls")</p>
<p>###########去重#####################<br>df=df.drop_duplicates(["班级","姓名"])#按照某两列去重<br>df=df.drop_duplicates()#完全相等的去重</p>
<p>###############重置索引###############<br>df=df.reset_index(drop=True)</p>
<p>##################      计时器   #######################################<br>import time<br>starttime = time.time()<br>#下面填写具体程序<br>time.sleep(2.1)#延时2.1s<br>#上面填写具体程序<br>endtime = time.time()<br>dtime = endtime - starttime<br>print("程序运行时间：%.8s s" % dtime)  #显示到微秒</p>
<p>##############################   时间戳    ###################################<br>#首先调用包，创建时间戳列<br>import pandas as pd <br>df=pd.DataFrame({"时间戳":[1529648412,1529648412]})</p>
<p>#时间戳<a href="https://blog.csdn.net/lilongsy/article/details/80242427?utm_source=blogxgwz7" target="_blank">按照周期加减一天</a><br>#比较好的<a href="https://blog.csdn.net/weixin_33861800/article/details/88001086" target="_blank">参考</a></p>
<p>###############①方法    时间戳(1529648412)列→(20180622) ##########################<br>import time<br>df['时间'] = df['时间戳'] .apply(lambda x:time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(x))) <br>df<br>###############②方法    时间戳(1529648412)列→(20180622)<br>df['时间']=pd.to_datetime(df['时间戳'],unit='s') #→(2018-06-22 06:20:12)<br>##(2018-06-22 06:20:12) → (2018-06-22)<br>df=df.set_index('时间')    #把时间设置为索引（为了转化）<br>df["当日日期"]=df.index.date  #转化为当日日期(2018-06-22)<br>df=df.reset_index()</p>
<p>###############③方法    时间戳(1529648412)列→(20180622)<br>################# <a href="https://www.jianshu.com/p/e4101420496f" target="_blank">Python 时间戳→当日/次日 日期</a>  ########################</p>
<p>####时间戳(1529648412)→(2018-06-22 06:20:12)→(2018-06-22)<br>df['时间']=pd.to_datetime(df['时间戳'],unit='s')<br>df=df.set_index('时间')    #把时间设置为索引（为了转化）<br>df["date"]=df.index.date   #转化为日期(2018-06-22)<br>df=df.reset_index() <br>#####(2018-06-22)→年月日三列→(20180622)<br>df["年"]=df["date"].map(str).str[0:4]<br>df["月"]=df["date"].map(str).str[5:7]<br>df["日"]=df["date"].map(str).str[8:]<br>df["日期"]=df["年"].map(str)+df["月"]+df["日"]<br>df</p>
<p>#####时间戳(1529648412)列→年月日三列→为(20180622)  (暂时行不通 )<br>df['year'] =df['时间戳'].apply(lambda x: time.localtime(x).tm_year) <br>df['month'] = df['时间戳'].apply(lambda x: time.localtime(x).tm_mon) <br>df['day'] =df['时间戳'].apply(lambda x: time.localtime(x).tm_mday)<br>#暂时行不通（2018+6+22): df3["创建日期"]=df3["year"].map(str)+df3["month"].map(str)+df3["day"].map(str)</p>
<p>###################④方法：任意时间格式→时间戳函数 <br>from datetime import datetime<br>#任意时间格式→时间戳函数<br>def time2stamp(cmnttime): <br>    cmnttime=datetime.strptime(cmnttime,'%Y%m%d') #这里可以更改任意格式：'%Y-%m-%d %H:%M:%S'    <br>    stamp=int(datetime.timestamp(cmnttime))<br>    return stamp<br>df['日期时间戳']=df['日期'].apply(time2stamp)</p>
<p>#########################          处理脏数据    ##############################<br>############①找出一列中的不同元素，并按照最大值输出#####<br>import pandas as pd<br>import numpy as np<br>df=pd.DataFrame({"id":["1","2","3,4"]})<br>df<br>#仅保留最大值<br>def max_str(t):<br>    a=[int(i) for i in t]<br>    return max(a)<br>df["id_max"]=df["id"].str.split(",").map(max_str)<br>df<br>############②找出一列中的不同元素，并按照最大值输出##### <br>#清理带有“,”的脏数据 <br>df2=df.astype(str)#令所有内容变成字符串<br>#清理广告行业id的脏数据<br>df3=df2[df2["广告行业id"].str.contains(",")]#包含逗号的数据<br>L1=list(df3.广告行业id)#包含逗号的数据做成一个列表list1<br>L2=list(df2.广告行业id)#全部数据做成一个列表list2<br>L3=list(set(L2)^set(L1))#列表求差集的方法：去掉脏数据的正常数据集合<br>df[df.广告行业id.isin(L3)]#isin()搜寻正常数据集合的最终结果<br>#直接方法:   ~isin()搜寻不包含异常值的最终结果<br>df[~df.广告行业id.isin(L1)]<br>############③找出一列中的不同元素，拆分输出#####<br>import pandas as pd<br>df=pd.DataFrame([["a","15"],["b,c","16"]],columns=["姓名","分数"])<br>df.set_index("分数")["姓名"].str.split(",",expand=True).stack().reset_index(level=1,drop=True).reset_index(name="姓名")<br>#############④处理空值NaN#######################<br>import pandas as pd,numpy as np  <br>df=pd.DataFrame({'姓名':'张三 李四 王五 赵六 孙七 马八'.split(),'年龄':[18,np.nan,22,23,11,np.nan]})<br>#找出空值 <br>isnull() <br>notnull()<br>#填充空值NaN为0 或者其他<br>df.fillna(0)<br>#删去指定列空值所在行或列删除空值行(axis=0)或列(axis=0)<br>df.dropna(axis=0, how="any", thresh=None, subset=None, inplace=False) <br>#当然可以直接简写<br>df.dropna()<br>#删去指定列空值所在行<br>df[np.isnan(df.年龄)==False ]<br>#值替换 <br>df.replace()</p>
<p>####################     条件筛选+分组统计     #############################<br>#找出满足条件的数据（年月相同，曝光日为创建的第二日）<br>df=df3[df3.创建日期.map(int)+1==df3.广告请求日期.map(int)]</p>
<p>#统计"相同ID"和"创建时间"的数据计数 (按照"广告id" 和 "创建日期"分组并计数) 即"次日曝光量"<br>df['次日曝光量']=df.groupby(['广告id','创建日期'])['广告id'].transform(len)<br>#方法二<br>counts=df.groupby(['id','日期']).size() #获得'id' '日期' "次日曝光量" 三列 这样的表counts<br>counts=df.reset_index(name='label') #重置索引（否则表头高度不一致）<br>df=df.merge(counts,how='inner',on=['id','日期'])#按照两列关联到一起，即得到“日曝光量”这一列</p>
<p>#按照max得分统计队伍<br>data2=data.groupby(["队伍名称"],as_index=False)["得分"].max()<br>#按照sum得分统计队伍 <br>data2=data.groupby(["队伍"],as_index=False)["得分"].sum()<br> #按照 个数 统计 队伍 <br>data.groupby(["队伍"]).size()</p>
<p>#增加一列 求和 <br>df["总分"]=df.sum(axis=1)<br>#排序（按照最终分数） <br>df=df.sort_values(by="总分",ascending=False) <br>#让索引从1开始<br>df.index = df.index+1</p>
<p>##############  像excel一样操作 按照某种条件 增加一列   ①############<br>def F(a):<br>    if a[1]&lt;a[2]: <br>        s='甲'<br>    if a[1]&gt;a[2]: <br>        s='乙' <br>    return s<br>df["完成人"]=[F(df.iloc[i]) for i in range(len(df.index))]</p>
<p>############   像excel一样操作 按照某种条件 增加一列  ② IF函数  ####<br>#对应的Excel语言： =IF(条件，分支1，分支2)<br>df1['category'] = np.where(df1['total'] &gt; 200000, 'A', 'B')</p>
<p># 提取前2位 到新的一列#</p>
<p>df[b] = df[a].apply(lambda x : x[:2])</p>
</blockquote><hr><p>题目：[1,2,3,1,1,1,0,0,0,2,2,2,1,1,1,1,1]中 求出最长的连续序列个是1 个数是5</p><blockquote><p>import pandas as pd<br>df=pd.DataFrame({"in":[1,2,3,1,1,1,0,0,0,2,2,2,1,1,1,1,1]})<br>df["out"]=df["in"].groupby((df["in"]!=df["in"].shift()).cumsum()).cumcount()+1<br>df[df["out"]&gt;4]</p></blockquote><div class="image-package">
<img name src="http://upload-images.jianshu.io/upload_images/17163699-d77593c6e3579689.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"><br><div class="image-caption"></div>
</div><p>1.  list1 = [1，2，3]</p><p>2.  list2 = [3，4，5]</p><p>3.  set1 = set(list1)    # 去重</p><p>4.  set2 = set(list2)    # 去重</p><p>5.  print(set(1)&amp;set(2))    ﻿﻿#交集</p><p>6.  print(set(1)^set(2))   #补集</p><p>7 set(1)-set(2) </p><p><a href="https://www.jianshu.com/p/2d49cb87626b" target="_blank">groupby, agg, apply</a>用法</p><p>def func(x):﻿</p><p>    if x&gt;20:</p><p>        return '20+k'</p><p>    else:</p><p>        return '0-20k'</p><p>position.apply(lambda x:func(x.avg),axis=1).head()</p><p>《pandas.apply实测使用方法》https://blog.csdn.net/weixin_39757737/article/details/78698316<br></p><p>If you are interested in this topic.﻿<br>You can get in touch with me.<br>18234056952(Tel  wechat  qq)</p>
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          <p><a href="https://www.python.org/ftp/python/3.7.3/python-3.7.3.exe" target="_blank">先下载Python</a>→另存为到桌面。(等待的时间开始做题）</p><p>一：<a href="http://yingkebao.top/web/formview/5cd2317a75a03c73a7d4b06a" target="_blank">上机考试一</a></p><p>二：<a href="https://paste.ubuntu.com/p/t7d7YvcxMw/" target="_blank">上机测试二</a></p><p>三:在Python上运行第二题→Python的<a href="https://www.jianshu.com/p/fc05889c12b5" target="_blank">安装与使用</a></p>
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          <p><a href="https://blog.csdn.net/w113691/article/details/80385534" target="_blank">matplotlib作图</a></p><p>柱状图</p><p><a href="https://www.jianshu.com/p/056ee00f6a99" target="_blank">新的库，感觉不错</a><br></p><p><a href="https://mp.weixin.qq.com/s?__biz=Mzg4ODA3OTcyNA==&amp;mid=2247483947&amp;idx=1&amp;sn=40278806bf80fec366768ecbf34accd5&amp;chksm=cf81d628f8f65f3ec4949a864008dd5cf303a37f3f41f4d1b45411e409ce3c970e86a7cb0fc7&amp;mpshare=1&amp;scene=2&amp;srcid=&amp;from=timeline&amp;ascene=2&amp;devicetype=android-28&amp;version=2700043a&amp;nettype=WIFI&amp;abtest_cookie=BAABAAoACwASABMABgAjlx4AVpkeAMyZHgDcmR4A8ZkeAAOaHgAAAA%3D%3D&amp;lang=zh_CN&amp;pass_ticket=RqQCX67T%2FAA8A5Hx4a6itB5Cyvi2jgp1OQQGuh1LPjojH4vjqYDO%2BcW%2B479a3Kcw&amp;wx_header=1" target="_blank">入门</a><br></p><p><a href="https://www.jianshu.com/p/295656fde8b6" target="_blank">seaborn</a><br></p><p><a href="https://www.jianshu.com/p/446860fd4418" target="_blank">matplotlib及seaborn实现可视化</a><br></p><p><br></p><p><a href="https://mp.weixin.qq.com/s?__biz=MzI5NDY1MjQzNA==&amp;mid=2247489449&amp;idx=4&amp;sn=340a179a367a19fdab793f6b5eedab68&amp;chksm=ec5ec8d4db2941c2b51bd7d46da2dc788d4d6aee3d35b26cd5c07cf5e52fae5b31d2d4bae2a0&amp;xtrack=1&amp;scene=0&amp;subscene=131&amp;clicktime=1558086020&amp;ascene=7&amp;devicetype=android-28&amp;version=2700043a&amp;nettype=cmnet&amp;abtest_cookie=BAABAAoACwASABMABgAjlx4AVpkeAMyZHgDcmR4A8ZkeAAOaHgAAAA%3D%3D&amp;lang=zh_CN&amp;pass_ticket=XV9RSZDu0WR64Jal4EdCNxL3UAg3AFuiUBgb2HePIg0V5WZDk6m3EnYx72oKY5mb&amp;wx_header=1" target="_blank">10个图片处理工具</a><br></p><hr id="null"><p>If you are interested in this topic.﻿<br>You can get in touch with me.﻿<br>18234056952(Tel  wechat  qq)</p>
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          <h1><b>利用python定时执行</b></h1><p>1 <a href="https://blog.csdn.net/dongfuguo/article/details/63680131" target="_blank">源代码循环</a></p><p>2 <a href="https://blog.csdn.net/liao392781/article/details/80521194" target="_blank">schedule函数包</a></p><blockquote>
<p>import schedule<br>import time</p>
<p>def job():<br>    print("I'm working...")</p>
<p>schedule.every(10).minutes.do(job)</p>
<p>schedule.every().hour.do(job)</p>
<p>schedule.every().day.at("10:30").do(job)</p>
<p>schedule.every(5).to(10).days.do(job)</p>
<p>schedule.every().monday.do(job)</p>
<p>schedule.every().wednesday.at("13:15").do(job)</p>
<p>while True:</p>
<p>    schedule.run_pending()</p>
<p>    time.sleep(1)</p>
</blockquote><hr id="null"><p><br></p><h1><b><a href="https://blog.csdn.net/sinat_36246371/article/details/62426444" target="_blank">利用Python下载文件</a></b></h1><h1><a href="https://www.cnblogs.com/aylin/p/6114818.html" target="_blank">python模拟登陆之下载</a></h1><hr id="null"><p>If you are interested in this topic.﻿<br>You can get in touch with me.﻿<br>18234056952(Tel  wechat  qq)</p>
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        <h1 class="title">Python下载安装 与 入门进阶</h1>
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          <h1>Python3.7.3下载安装(推荐使用下文其他方法)</h1><p><b>一</b>. <a href="https://www.python.org/ftp/python/3.7.3/python-3.7.3.exe" target="_blank">官网下载</a>,其他途径:①<a href="https://t.zsxq.com/AMf6miM" target="_blank">知识星球下载</a>②<a href="https://share.weiyun.com/5nIqjZy" target="_blank">腾讯微云下载</a>。(以上是Windos_32位下载版本，其余所有版本在<a href="https://www.python.org/ftp/python/3.7.3/" target="_blank">这里</a>)<br><b>二</b>. <a href="https://www.jianshu.com/p/bf0199609ebb" target="_blank">安装</a>教程<br><b>三</b>. 如何运行程序:(编译界面软件有很多，任选其一)<br>（1）使用自带IDLE编译界面来运行程序(Python考级必备)：①打开左下角开始<a href="https://jingyan.baidu.com/article/49ad8bcea4b2635834d8fa88.html" target="_blank">找到Python IDLE</a>，②<a href="https://baijiahao.baidu.com/s?id=1605782875425735021&amp;wfr=spider&amp;for=pc" target="_blank">操作运行方法</a><br>（2）<a href="http://fastsoft.onlinedown.net/down/pycharmpro2018.3.5.exe" target="_blank">下载</a>并<a href="https://blog.csdn.net/hq_nuan/article/details/83653677" target="_blank">安装Pycharm编译界面</a>，接着<a href="https://www.cnblogs.com/beginner-boy/p/7785041.html" target="_blank">首次运行</a>，<a href="https://www.jianshu.com/p/2b11fa62f1fc" target="_blank">完成第一个程序</a></p><h1>其他Python方法：</h1><p><b>一. 无需下载Python，<a href="https://jingyan.baidu.com/article/eae078275a31851fec5485b8.html" target="_blank">直接下载Anaconda</a>（自带很多Puthon库 非常方便，界面友好）<a href="https://www.jianshu.com/p/a85bc2a8fa56" target="_blank">Jupyter使用技巧</a><br>（1）下载镜像1<a href="https://mirrors.ustc.edu.cn/anaconda/pkgs/free/" target="_blank">https://mirrors.ustc.edu.cn/anaconda/pkgs/free/</a><br>（2）下载镜像2<a href="https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/" target="_blank">https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/</a><br>（3）下载镜像3<a href="https://mirrors.ustc.edu.cn/anaconda/cloud/msys2/" target="_blank">https://mirrors.ustc.edu.cn/anaconda/cloud/msys2/</a><br>二</b>. 什么都不下载, 使用网页编译Python:<br>（1）<a href="https://www.jianshu.com/p/786a8adeefa5" target="_blank">微软免费远程服务</a>:无需安装，联网即用，运算快，界面同Anaconda。<br>（2）<a href="https://mp.weixin.qq.com/s?fontRatio=1&amp;__biz=MzIyNjM2MzQyNg==&amp;mid=2247484531&amp;idx=1&amp;sn=befca463530ba0d017493e3072fadde1&amp;scene=94&amp;subscene=315&amp;passparam=searchid%3D7454146522759176618&amp;ascene=0&amp;devicetype=android-28&amp;version=2700043a&amp;nettype=cmnet&amp;abtest_cookie=BAABAAoACwASABMABQAmlx4AVpkeAMyZHgDcmR4A8ZkeAAAA&amp;lang=zh_CN&amp;pass_ticket=TL3UbZeGzD2Pz5tVNfza3BoOvVU%2FkNSh878s7mKmnef0mq%2BKSrQeVDXvzccwKw5p&amp;wx_header=1" target="_blank">Google免费远程服务</a>:提供GPU选项和教程<br><b>三</b>. 手机app: Pydroid3</p><hr><h1>入门进阶:</h1><h4>1. <a href="https://www.jianshu.com/p/30d4cde974f6" target="_blank">入门</a>:  <a href="https://crossincode.com/course/lesson_list/" target="_blank">python入门</a> +<a href="https://docs.python.org/zh-cn/3/" target="_blank">官方中文使用文档</a> + <a href="https://blog.csdn.net/weixin_41084236/article/details/81564963" target="_blank">迅速入门100个例</a>
</h4><h4>2. 进阶: <a href="https://github.com/china-testing/python-api-tesing/blob/master/books.md#python" target="_blank">大量书籍</a> + <a href="https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&amp;mid=2650761248&amp;idx=1&amp;sn=25b306cb5e4e39730e960f109bfaf044&amp;chksm=871aac5eb06d2548a75eda9f2accf50aad8be315f71514c347fec2fc0b9b17a3ea898a4ba3eb&amp;mpshare=1&amp;scene=2&amp;srcid=0428HfKljka4YCXj3j1Y1vHO&amp;from=timeline&amp;ascene=2&amp;devicetype=android-27&amp;version=27000439&amp;nettype=cmnet&amp;abtest_cookie=BAABAAoACwASABMABQAmlx4AzJkeANKZHgDcmR4A7pkeAAAA&amp;lang=zh_CN&amp;pass_ticket=ieZF52e%2FhVvjzGcJHHpbJ4%2B7obOZfhqAugLFI9u2%2FLkOyCSBYGJxAFoHNd%2B1okTX&amp;wx_header=1" target="_blank">AI学习路径</a>+<a href="https://software.intel.com/zh-cn/ai/courses" target="_blank">Intel人工智能课程</a>
</h4><hr><p>If you are interested in this topic.﻿ <br>You can get in touch with me.﻿ <br>18234056952(Tel wechat qq)</p>
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